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Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.
Abstract: Like many service industries, the financial industry is largely characterized
by administrative and back-office processes and distinguished by a broad systems
landscape with a high proportion of legacy systems. Missing interfaces between information
systems, user interfaces, or web applications often require many manual
activities. As banks are often functionally organized into traditional departments, a
process-oriented organizational structure is rarely in place. The financial industry
therefore offers enormous potential for the use of robotic process automation (RPA)
and the raising of potential benefits such as process-related cost savings, time reductions,
and quality improvements.
The aim of this chapter is to describe the tremendous opportunities that the use of
RPA technology offers to the financial industry and to explain how these opportunities
can be realized. Therefore, we start by explaining the challenges that progressive digitalization
poses to the industry and how RPA, but also more advanced technologies
(that work not only rule-based but also define own rules), such as artificial intelligence,
can help to overcome them. As well as providing an overview of the various
applications of RPA in the financial industry, we also provide a comprehensive case
study of a relevant practical application
Like many service industries, the financial industry is largely characterized by administrative and back-office processes and distinguished by a broad systems landscape with a high proportion of legacy systems. Missing interfaces between Information Systems, User Interfaces or Web Applica-tions often require many manual activities. As banks are often functionally organized into traditional departments, a process-oriented organizational structure is rarely in place. The financial industry therefore offers enormous potential for the use of Robotic Process Automation (RPA) and the rais-ing of potential benefits such as process-related cost savings, time reductions and quality im-provements.
The aim of this chapter is to describe the tremendous opportunities that the use of RPA technology offers to the financial industry and to explain how these opportunities can be realized. Therefore, we start by explaining the challenges that progressive digitalization poses to the industry and how RPA, but also more advanced technologies (that work not only rule-based but also define own rules), such as Artificial Intelligence (AI), can help to overcome them. As well as providing an over-view of the various applications of RPA in the financial industry, we also provide a comprehensive case study of a relevant practical application.
One domain of application of artificial intelligence (AI) is decision support, particularly in management. Although there are already research streams examining the interaction of AI and humans (e.g. the stream on "hybrid intelligence"), there are still numerous open research gaps – for example, a comprehensive overview of which factors favor the intention to use AI is missing. By conducting a systematic literature review, we identify the factors that potentially positively influence AI usage intentions for decision-making processes in organizations. From this, we create a framework that both provides practical implications for the successful use of AI in organizational decision-making processes and delivers further research approaches, for example, on the validity/ usability of proven IS adoption models in the present context.